OpenAI's decision to retire GPT-4o has triggered intense backlash, revealing deep emotional attachments users form with AI systems and raising critical questions about synthetic companion safety.
A comprehensive guide to evaluating AI agents covering benchmarks, testing frameworks, and metrics for measuring autonomous system performance in real-world applications.
Oscar-nominated director Darren Aronofsky embraces AI video generation for historical documentary filmmaking, signaling a significant shift in Hollywood's approach to synthetic media production.
Researchers propose rethinking how evaluation rubrics are generated for LLM judges and reward models, addressing critical challenges in assessing open-ended AI outputs.
New research reveals that standard LoRA fine-tuning can achieve performance comparable to sophisticated variants when learning rates are properly optimized, challenging assumptions about adapter complexity.
Researchers propose a novel approach to improve LLM reasoning by discovering and replaying latent actions, potentially reducing inference costs while maintaining reasoning quality.
New research presents A²-LLM, an end-to-end framework that unifies conversational AI with audio avatar generation, enabling seamless speech-driven digital humans through large language models.
Indonesia calls for coordinated ASEAN response to AI-generated deepfakes and disinformation, signaling potential regional framework for synthetic media governance in Southeast Asia.
In a remarkable timing coincidence, OpenAI launched its new agentic coding model just minutes after Anthropic released its own, signaling intensifying competition in AI-powered software development.
Anthropic releases Claude Opus 4.6 with major improvements in coding and agentic task handling, advancing autonomous AI capabilities for complex multi-step workflows.
Mistral AI launches Voxtral Transcribe 2, combining batch speaker diarization with open real-time automatic speech recognition for multilingual production workloads at enterprise scale.
New research on MultiKrum explores optimal robustness definitions for Byzantine machine learning, critical for securing distributed AI training against adversarial participants.